Electrical demand in a house depends on various factors mainly being the user’s behaviour and the rating of the appliances. Some researchers have used daily domestic electrical demand profile at half hour time resolution for the energy management. When data of half hour time interval is used for the analysis of on-site generation, it can lead to over/under -estimates of the proportion of generated energy used on site. As a consequence, this could lead to over/under-estimating in the import and export of power from and to the power grid. In this paper, domestic electricity use profile recorded at high time resolution of one minute is used to analyse the profile obtained at different time resolution and its effect on on-site generation.

 Daily load profile for summer and winter at time resolution of 30 minute is generated from a data set of 22 houses consisting data of a whole year which is then compared with the daily load curve obtained after diversity maximum demand from the literature. The generated daily load profile is then used to see effect on the low voltage network. For the analysis on the low voltage network, a typical UK low voltage network is developed in the Matlab/Simulink softwar

Karmacharya, Samir

Mahkamov, Khamid

Putrus, Ghanim

Underwood, Chris

English

Northumbria Research Link

Evaluation of Domestic Electrical Demand and Its Effect on Low Voltage Network  S. Karmacharya School of Computing Engineering and Information Sciences Northumbria University, UK samir.karmacharya@northumbria.ac.uk G. Putrus School of Computing Engineering and Information Sciences Northumbria University, UK                                 C. Underwood School of Built and Natural Environment Northumbria University, UK  c                                 K. Mahkamov School of Computing Engineering and Information Sciences Northumbria University, UK khamid.mahkamov@northumbria.ac.uk  Abstract - Electrical demand in a house depends on various factors mainly being the user’s behaviour and the rating of the appliances. Some researchers have used daily domestic electrical demand profile at half hour time resolution for the energy management. When data of half hour time interval is used for the analysis of on-site generation, it can lead to over/under -estimates of the proportion of generated energy used on site. As a consequence, this could lead to over/under-estimating in the import and export of power from and to the power grid. In this paper, domestic electricity use profile recorded at high time resolution of one minute is used to analyse the profile obtained at different time resolution and its effect on on-site generation.  Daily load profile for summer and winter at time resolution of 30 minute is generated from a data set of 22 houses consisting data of a whole year which is then compared with the daily load curve obtained after diversity maximum demand from the literature. The generated daily load profile is then used to see effect on the low voltage network. For the analysis on the low voltage network, a typical UK low voltage network is developed in the Matlab/Simulink software.  Index Terms — Domestic load profile, Distribution network, Electrical load profile, Low voltage network.  I. INTRODUCTION  Energy efficiency measures and application of emergent renewable energy technologies have made the energy end-user increasing important and influential on system operation. Electrical demand for a dwelling varies between households and spread of demand levels for different end-users over time varies over days and seasons, weekdays and weekends and loads coincidence. The breakdown of the use of electricity in a single household depends of mainly three factors: set of appliances in the household; the electrical rating of these appliances and the use of the appliances. The most unpredictable and complex among these three is the use of the appliances which highly depends on the behaviour of the occupants. In most of the literature [1, 2] the time interval for each data of electrical load is half hour, which is the standard interval for load analysis and electricity trading in UK industry. Half hour resolution of electrical load is sufficient to show the load aggregated over the hundreds of customers such as at the point of distribution transformer. However, this aggregated load over of half hourly resolution tends to hide the high-frequency variations or spikes in loads over time in an individual building. In dwelling particularly, loads can vary greatly over a few minutes due to many appliances of higher rating and their pattern of use. On top of that, on-site generation, e.g. micro-combined heat and power (CHP), photovoltaic and micro-wind which have been very popular for dwellings, has increased the importance to understand the nature of domestic loads for appropriate energy management and supply/demand matching [3]. This paper compares the daily load profile obtained from two different time resolution 1 minute and 30 minute. Furthermore, the impact of using 30 minute time resolution load curve for the evaluation of on-site generation is analysed.   II. DATA SET Data set of 1-min load (kW) were available from UK Data Archive  for 22 dwelling in East Midlands of England between January 2008 and December 2009, with varying amount of data missing. Three different types of dwelling were included in the data set: detached, semi-detached and terraced with number of occupants varying from 1 to 6. Suitable assumption was made to replace the missing data in the data set such as, if any monthly, weekly, daily or hourly data was missing then data from the corresponding previous month, week, day or hours was taken to fill the missing data for instance, if the data of Monday in second week of May is missing then Monday of first week of May was taken to fill up the missing data.  III. DOMESTIC LOAD PROFILE  AND TIME RESOLUTION Electrical load on a house depends on various factors such as occupancy social status, size of house, number of occupancy, occupancy pattern, time of day and year, geographical location and ownership of appliances. It is the product of complex interaction between pattern of appliances used and the load themselves. For example, refrigerator is always connected but they are cyclic load whereas there are some loads which are constant. The residential electricity demand pattern can be generally characterised by a small base load with short peaks of high demand.  Different models for domestic electricity demand have been developed in the past. Richardson et al. [4] have developed a domestic electricity demand model based on the occupant time use data. Whereas, the model developed by Paatero and Lund [5] uses the appliances as the basis. Among 22 houses, one house with typically high-demand and high base-load is selected. Different electrical appliances such as washing machine, electric shower, kettle, electric oven, microwave, toaster, dish wash, washer dryer along with timer were present in the selected house. A typical load profile for a day of February at 1 minute intervals and corresponding half-hour intervals is shown in Fig. 1. The same data when plotted at different time resolutions shows different load patterns which is clear from Fig. 1. Comparing these two profiles shows how half-hour interval data is smooth and smaller than the 1 minute interval data. A large load which operates for longer time such as a washing machine (operating in the early morning as shown in Fig. 1) was captured in the half-hourly interval whereas large loads with a short interval such as the spike after 8 and 21 could easily be missed.    0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24024681012Comparison of Minute and Half-hourly DataTime (hr)Power (kW)  1 Minute dataHalf Hour data  Fig. 1 Load profile at 1 Minute and 30 Minute time resolution  If assumed that house has an micro-combined heat and power which has electrical output of 1.5 kW (shown by black dot line in figure 2), half-hour data profile suggests that all the electrical loads in the house between 10:30 to 15:00 would be met by the micro-CHP and the surplus electricity can be exported or stored all time between the given period. While 1 minute data profile shows different picture of electricity import and export during the given time interval, all loads would not be met and there would be significant import of electricity from the grid between 11:00 and 12:00 and before 15:00. Power consumed by same house on the same day using the two profiles (1 minute and 30 minute interval) give two different results.  10 11 12 13 14 15 16012345678910Comparison of Minute and Half-hourly DataTime (hr)Power (kW)  1 Minute dataHalf Hour data Fig. 2 Comparison of 1Minute and 30 Minute time resolution with 1.5 kW on-site generation  Similarly, if micro-CHP is assumed to operate for 24 hours then on the basis of 1 minute data load curve 38426.4 kWh of energy is supplied by the on-site generation whereas half-hourly load curve suggest 37317.6 kWh of energy supplied by the on-site generation. As a consequence of this the feasibility study of on-site generation could also be affected.   IV. LOAD CURVE COMPARISON From the available data set, summer load curve and winter load curve for 22 houses was obtained at an interval of 30 minute. The data set was first completing for a whole one year for all 22 houses by filling up the missing data with suitable assumption as mentioned in earlier section. Then mean daily curve for each month was obtained from the sum of all the house loads. In UK, June, July and August is consider as summer and December, January and February as winter so taking means of the respective months for corresponding seasons, summer load curve and winter load curves are obtained.  The obtained curves were then compared with the after diversity maximum demand (ADMD) daily load curves for summer and winter as shown in figure 3 and 4 respectively. Af      v     y   x                 f        “    maximum demand, per customer, as the number of customers                                       f    y”[6]. The ADMD is often used for the design of electricity distribution networks where large numbers of customers are connected.  The comparison in the curves shows some similarity in the patter of the curves, loads are higher in the morning (getting ready for school/work) and in the evening (back from work/dinner time), least in the early morning (sleeping time) of a day and decreases at the night time. For summer, the load profile obtained from 22 houses is higher than the ADMD load curve and its other way for winter. This may be due to number of houses considered as ADMD load curve is obtained on the basis of load at the point of distribution transformer in the low voltage network which considers hundreds of houses. However, the main difference observed is the difference in time when the load starts to increase in the morning. From figure 3 it can be seen that the obtained curve start to increase after 5 a.m., which looks more realistic, where as in ADMD curve the increase in load starts after 7 am only.   0 5 10 15 20 240.150.20.250.30.350.40.450.50.550.60.65Half Hourly Summer Load Curve ComparisonTime (hr)Power (kW)  Half Hourly Load CurveADMD Load Curve  Fig. 3 Comparison of half hourly summer load curve  0 5 10 15 20 240.20.40.60.811.2Half Hourly Winter Load Curve ComparisonTime (hr)Power (kW)  Half Hourly Load CurveADMD Load Curve  Fig. 4 Comparison of half hourly winter load curve V. EFFECT ON LOW VOLTAGE NETWORK To access the difference in the impact of the two load curves for winter and summer in the low voltage network, a typical UK distribution network, as shown in figure 4, which includes voltage levels between 33 kW and 400 V is used. The network includes a 33 kV (500 MVA) source as a grid supply point, which is connected to a substation consisting of two 33/11 kV transformers equipped with on-load tap changers. The substation supplies six 11 kV outgoing feeders and each 11 kV feeders supplies eight low voltage distribution substations. Each low voltage distribution substation consist of 11/0.4 kV (500 kVA) transformer which is an off-load transformer.  As the impact was to access in the low voltage side of the network, one of the six 11 kV feeder together with its connected loads were modelled in detail and other five were modelled as lumped load connected to 11 kV busbar. Similarly, one of the eight 11/0.4 kV substation was modelled in detail and others were modelled as individual lumped loads. Each 11/0.4 kV substation supplied 384 customer from its four outgoing feeders, resulting total load connected to 33/11 kV substation to be 384×8×6=18,432 domestic customers.  The above mentioned low voltage distribution network was modelled using Matlab/Simulink as shown in figure 5. „L      400V f       f 11  V‟  l       f      5      subsystem of all lumped loads consisting of 11/0.4 kV                         11  V f          „l   v l     f      400 V‟  l                  f f      5          y              detail of 400 kV feeder is modelled.       Fig. 5 Matlab/Simulink model of typical low voltage network in UK The modelled low voltage network was simulated for 24 hours with summer and winter load curves obtained from the data set. Voltage at each busbar was measured during the simulation however, the voltage profile at the beginning and far end of 400 V feeder, i.e. busbar 11 (B11) and busbar 17 (B17), is only presented for summer and winter in Figure 6 and 7 respectively. The voltage profiles indicate that the voltage level remains within the statuary limits (1.06 p.u. and 0.94 p.u. for low voltage network) of the nominal voltage for the summer curve. In case of winter load curve, the voltage at busbar 11 remains within the limit but voltage at busbar 17 goes below the statuary limit around 18:00 when more loads are connected.  0 5 10 15 200.960.9650.970.9750.980.9850.990.9951Voltage comparison with summer load curveTime (hr)Voltage (p.u.)   B17 B11 Fig. 6 Voltage profile at busbar 11 and 17 with summer load curves   0 5 10 15 200.930.940.950.960.970.980.991Voltage comparison with winter load curvesTime (hr)Voltage (p.u.)  B17B11  Fig. 7 Voltage profile at busbar 11 and 17 with winter load curves  VI. CONCLUSION Domestic daily load profile of same house on same day with two different time resolution of 1 minute and 30 minute is presented in the paper. Disadvantages of using 30 minute time resolution daily load profile over 1 minute load profile is highlighted. Key point noted when using daily load curve with 30 minute time resolution is that it tends to hide loads with short interval and number of appliances which operates for short interval is high in dwelling. Along with that for on-site generation, using 30 minute load curve tends to under estimate the total amount of energy supplied to the house then using 1 minute load curve which further will affect the feasibility of installing on-site generation in the dwellings. Summer and winter daily load curves were generated using the data obtained by monitoring the power of 22 houses. The curves were then compared with the respective seasonal ADMD daily load curve which shows the similarity of the patter.  REFERENCES [1] C. Barbier, A. Maloyd, and G. Putrus, "Embedded controller for LV network with distributed generation," Econnect Ventures Ltd., Department of Trade and IndustryMay 2007. [2] M. MacDonald, "System Integration of Additional Micro-generation (SIAM)," Department of Trade and Industry DG/CG/00028/REP, 2004. [3] A. S. Bahaj, L. Myers, and P. A. B. James, "Urban energy generation: Influence of micro-wind turbine output on electricity consumption in buildings," Energy and Buildings, vol. 39, pp. 154-165, 2007. [4] I. Richardson, M. Thomson, D. Infield, and C. Clifford, "Domestic electricity use: A high-resolution energy demand model," Energy and Buildings, vol. In Press, Corrected Proof, 2010. [5] J. V. Paatero and P. D. Lund, "A model for generating household electricity load profiles," International Journal of Energy Research, vol. 30, pp. 273-290, 2006. [6] D. H. O. McQueen, P. R. Hyland, and S. J. Watson, "Monte Carlo simulation of residential electricity demand for forecasting maximum demand on distribution networks," Power Systems, IEEE Transactions on, vol. 19, pp. 1685-1689, 2004.   

A model for generating household electricity load profiles,&quot;

Domestic electricity use: A high-resolution energy demand model,&quot; Energy and Buildings, vol.

Embedded controller for LV network with distributed generation,&quot;

Monte Carlo simulation of residential electricity demand for forecasting maximum demand on distribution networks,&quot; Power Systems,

System Integration of Additional Microgeneration (SIAM),&quot;

Urban energy generation: Influence of micro-wind turbine output on electricity consumption in buildings,&quot;

Evaluation of domestic electrical demand and its effect on low voltage network performance

Northumbria Research Link
Citation:  Karmacharya,  Samir,  Putrus, Ghanim, Underwood, Chris and Mahkamov, Khamid (2012) 
Evaluation of domestic electrical demand and its effect on low voltage network performance. In: 47th 
Universities' Power Engineering Conference (UPEC 2012), 4-7 September 2012, Brunel University,  
London, UK. 
URL: http://upec2012.com/programme.html <http://upec2012.com/programme.html>
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Evaluation of Domestic Electrical Demand and Its 
Effect on Low Voltage Network 
 
S. Karmacharya 
School of Computing Engineering 
and Information Sciences 
Northumbria University, UK 
samir.karmacharya@northumbria.ac.uk 
G. Putrus 
School of Computing Engineering 
and Information Sciences 
Northumbria University, UK 
                                
C. Underwood 
School of Built and Natural 
Environment 
Northumbria University, UK 
 c                                 
K. Mahkamov 
School of Computing Engineering 
and Information Sciences 
Northumbria University, UK 
khamid.mahkamov@northumbria.ac.uk 
 
Abstract - Electrical demand in a house depends on various 
factors mainly being the user’s behaviour and the rating of the 
appliances. Some researchers have used daily domestic electrical 
demand profile at half hour time resolution for the energy 
management. When data of half hour time interval is used for 
the analysis of on-site generation, it can lead to over/under -
estimates of the proportion of generated energy used on site. As 
a consequence, this could lead to over/under-estimating in the 
import and export of power from and to the power grid. In this 
paper, domestic electricity use profile recorded at high time 
resolution of one minute is used to analyse the profile obtained 
at different time resolution and its effect on on-site generation. 
 Daily load profile for summer and winter at time resolution 
of 30 minute is generated from a data set of 22 houses consisting 
data of a whole year which is then compared with the daily load 
curve obtained after diversity maximum demand from the 
literature. The generated daily load profile is then used to see 
effect on the low voltage network. For the analysis on the low 
voltage network, a typical UK low voltage network is developed 
in the Matlab/Simulink software. 
 
Index Terms — Domestic load profile, Distribution network, 
Electrical load profile, Low voltage network. 
 
I. INTRODUCTION 
 Energy efficiency measures and application of emergent 
renewable energy technologies have made the energy end-
user increasing important and influential on system operation. 
Electrical demand for a dwelling varies between households 
and spread of demand levels for different end-users over time 
varies over days and seasons, weekdays and weekends and 
loads coincidence. The breakdown of the use of electricity in 
a single household depends of mainly three factors: set of 
appliances in the household; the electrical rating of these 
appliances and the use of the appliances. The most 
unpredictable and complex among these three is the use of the 
appliances which highly depends on the behaviour of the 
occupants. 
In most of the literature [1, 2] the time interval for each 
data of electrical load is half hour, which is the standard 
interval for load analysis and electricity trading in UK 
industry. Half hour resolution of electrical load is sufficient to 
show the load aggregated over the hundreds of customers 
such as at the point of distribution transformer. However, this 
aggregated load over of half hourly resolution tends to hide 
the high-frequency variations or spikes in loads over time in 
an individual building. In dwelling particularly, loads can 
vary greatly over a few minutes due to many appliances of 
higher rating and their pattern of use. On top of that, on-site 
generation, e.g. micro-combined heat and power (CHP), 
photovoltaic and micro-wind which have been very popular 
for dwellings, has increased the importance to understand the 
nature of domestic loads for appropriate energy management 
and supply/demand matching [3]. 
This paper compares the daily load profile obtained from 
two different time resolution 1 minute and 30 minute. 
Furthermore, the impact of using 30 minute time resolution 
load curve for the evaluation of on-site generation is 
analysed. 
  
II. DATA SET 
Data set of 1-min load (kW) were available from UK Data 
Archive  for 22 dwelling in East Midlands of England 
between January 2008 and December 2009, with varying 
amount of data missing. Three different types of dwelling 
were included in the data set: detached, semi-detached and 
terraced with number of occupants varying from 1 to 6. 
Suitable assumption was made to replace the missing data in 
the data set such as, if any monthly, weekly, daily or hourly 
data was missing then data from the corresponding previous 
month, week, day or hours was taken to fill the missing data 
for instance, if the data of Monday in second week of May is 
missing then Monday of first week of May was taken to fill 
up the missing data. 
 
III. DOMESTIC LOAD PROFILE  AND TIME RESOLUTION 
Electrical load on a house depends on various factors such 
as occupancy social status, size of house, number of 
occupancy, occupancy pattern, time of day and year, 
geographical location and ownership of appliances. It is the 
product of complex interaction between pattern of appliances 
used and the load themselves. For example, refrigerator is 
always connected but they are cyclic load whereas there are 
some loads which are constant. The residential electricity 
demand pattern can be generally characterised by a small 
base load with short peaks of high demand.  
Different models for domestic electricity demand have 
been developed in the past. Richardson et al. [4] have 
developed a domestic electricity demand model based on the 
occupant time use data. Whereas, the model developed by 
Paatero and Lund [5] uses the appliances as the basis. 
Among 22 houses, one house with typically high-demand 
and high base-load is selected. Different electrical appliances 
such as washing machine, electric shower, kettle, electric 
oven, microwave, toaster, dish wash, washer dryer along with 
timer were present in the selected house. A typical load 
profile for a day of February at 1 minute intervals and 
corresponding half-hour intervals is shown in Fig. 1. The 
same data when plotted at different time resolutions shows 
different load patterns which is clear from Fig. 1. Comparing 
these two profiles shows how half-hour interval data is 
smooth and smaller than the 1 minute interval data. A large 
load which operates for longer time such as a washing 
machine (operating in the early morning as shown in Fig. 1) 
was captured in the half-hourly interval whereas large loads 
with a short interval such as the spike after 8 and 21 could 
easily be missed.  
 
 
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
2
4
6
8
10
12
Comparison of Minute and Half-hourly Data
Time (hr)
P
o
w
er
 (
k
W
)
 
 
1 Minute data
Half Hour data
 
 
Fig. 1 Load profile at 1 Minute and 30 Minute time resolution 
 
If assumed that house has an micro-combined heat and 
power which has electrical output of 1.5 kW (shown by black 
dot line in figure 2), half-hour data profile suggests that all 
the electrical loads in the house between 10:30 to 15:00 
would be met by the micro-CHP and the surplus electricity 
can be exported or stored all time between the given period. 
While 1 minute data profile shows different picture of 
electricity import and export during the given time interval, 
all loads would not be met and there would be significant 
import of electricity from the grid between 11:00 and 12:00 
and before 15:00. 
Power consumed by same house on the same day using the 
two profiles (1 minute and 30 minute interval) give two 
different results.  
10 11 12 13 14 15 16
0
1
2
3
4
5
6
7
8
9
10
Comparison of Minute and Half-hourly Data
Time (hr)
P
o
w
er
 (
k
W
)
 
 
1 Minute data
Half Hour data
 
Fig. 2 Comparison of 1Minute and 30 Minute time resolution with 1.5 kW 
on-site generation 
 
Similarly, if micro-CHP is assumed to operate for 24 hours 
then on the basis of 1 minute data load curve 38426.4 kWh of 
energy is supplied by the on-site generation whereas half-
hourly load curve suggest 37317.6 kWh of energy supplied 
by the on-site generation. As a consequence of this the 
feasibility study of on-site generation could also be affected.  
 
IV. LOAD CURVE COMPARISON 
From the available data set, summer load curve and winter 
load curve for 22 houses was obtained at an interval of 30 
minute. The data set was first completing for a whole one 
year for all 22 houses by filling up the missing data with 
suitable assumption as mentioned in earlier section. Then 
mean daily curve for each month was obtained from the sum 
of all the house loads. In UK, June, July and August is 
consider as summer and December, January and February as 
winter so taking means of the respective months for 
corresponding seasons, summer load curve and winter load 
curves are obtained.  
The obtained curves were then compared with the after 
diversity maximum demand (ADMD) daily load curves for 
summer and winter as shown in figure 3 and 4 respectively. 
Af      v     y   x                 f        “    
maximum demand, per customer, as the number of customers 
                                      f    y”[6]. The 
ADMD is often used for the design of electricity distribution 
networks where large numbers of customers are connected. 
 The comparison in the curves shows some similarity in the 
patter of the curves, loads are higher in the morning (getting 
ready for school/work) and in the evening (back from 
work/dinner time), least in the early morning (sleeping time) 
of a day and decreases at the night time. For summer, the load 
profile obtained from 22 houses is higher than the ADMD 
load curve and its other way for winter. This may be due to 
number of houses considered as ADMD load curve is 
obtained on the basis of load at the point of distribution 
transformer in the low voltage network which considers 
hundreds of houses. However, the main difference observed 
is the difference in time when the load starts to increase in the 
morning. From figure 3 it can be seen that the obtained curve 
start to increase after 5 a.m., which looks more realistic, 
where as in ADMD curve the increase in load starts after 7 
am only.  
 
0 5 10 15 20 24
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
Half Hourly Summer Load Curve Comparison
Time (hr)
P
o
w
er
 (
k
W
)
 
 
Half Hourly Load Curve
ADMD Load Curve
 
 
Fig. 3 Comparison of half hourly summer load curve 
 
0 5 10 15 20 24
0.2
0.4
0.6
0.8
1
1.2
Half Hourly Winter Load Curve Comparison
Time (hr)
P
o
w
er
 (
k
W
)
 
 
Half Hourly Load Curve
ADMD Load Curve
 
 
Fig. 4 Comparison of half hourly winter load curve 
V. EFFECT ON LOW VOLTAGE NETWORK 
To access the difference in the impact of the two load 
curves for winter and summer in the low voltage network, a 
typical UK distribution network, as shown in figure 4, which 
includes voltage levels between 33 kW and 400 V is used. 
The network includes a 33 kV (500 MVA) source as a grid 
supply point, which is connected to a substation consisting of 
two 33/11 kV transformers equipped with on-load tap 
changers. The substation supplies six 11 kV outgoing feeders 
and each 11 kV feeders supplies eight low voltage 
distribution substations. Each low voltage distribution 
substation consist of 11/0.4 kV (500 kVA) transformer which 
is an off-load transformer.  
As the impact was to access in the low voltage side of the 
network, one of the six 11 kV feeder together with its 
connected loads were modelled in detail and other five were 
modelled as lumped load connected to 11 kV busbar. 
Similarly, one of the eight 11/0.4 kV substation was modelled 
in detail and others were modelled as individual lumped 
loads. Each 11/0.4 kV substation supplied 384 customer from 
its four outgoing feeders, resulting total load connected to 
33/11 kV substation to be 384×8×6=18,432 domestic 
customers.  
The above mentioned low voltage distribution network was 
modelled using Matlab/Simulink as shown in figure 5. 
„L      400V f       f 11  V‟  l       f      5      
subsystem of all lumped loads consisting of 11/0.4 kV 
                        11  V f          „l   v l     f      
400 V‟  l                  f f      5          y              
detail of 400 kV feeder is modelled. 
 
 
 
 
 
 
Fig. 5 Matlab/Simulink model of typical low voltage network in UK 
The modelled low voltage network was simulated for 24 
hours with summer and winter load curves obtained from the 
data set. Voltage at each busbar was measured during the 
simulation however, the voltage profile at the beginning and 
far end of 400 V feeder, i.e. busbar 11 (B11) and busbar 17 
(B17), is only presented for summer and winter in Figure 6 
and 7 respectively. The voltage profiles indicate that the 
voltage level remains within the statuary limits (1.06 p.u. and 
0.94 p.u. for low voltage network) of the nominal voltage for 
the summer curve. In case of winter load curve, the voltage at 
busbar 11 remains within the limit but voltage at busbar 17 
goes below the statuary limit around 18:00 when more loads 
are connected. 
 
0 5 10 15 20
0.96
0.965
0.97
0.975
0.98
0.985
0.99
0.995
1
Voltage comparison with summer load curve
Time (hr)
V
o
lt
ag
e 
(p
.u
.)
 
 
 B17
 B11
 
Fig. 6 Voltage profile at busbar 11 and 17 with summer load curves 
 
 
0 5 10 15 20
0.93
0.94
0.95
0.96
0.97
0.98
0.99
1
Voltage comparison with winter load curves
Time (hr)
V
o
lt
ag
e 
(p
.u
.)
 
 
B17
B11
  
Fig. 7 Voltage profile at busbar 11 and 17 with winter load curves 
 
VI. CONCLUSION 
Domestic daily load profile of same house on same day 
with two different time resolution of 1 minute and 30 minute 
is presented in the paper. Disadvantages of using 30 minute 
time resolution daily load profile over 1 minute load profile is 
highlighted. Key point noted when using daily load curve 
with 30 minute time resolution is that it tends to hide loads 
with short interval and number of appliances which operates 
for short interval is high in dwelling. Along with that for on-
site generation, using 30 minute load curve tends to under 
estimate the total amount of energy supplied to the house then 
using 1 minute load curve which further will affect the 
feasibility of installing on-site generation in the dwellings. 
Summer and winter daily load curves were generated using 
the data obtained by monitoring the power of 22 houses. The 
curves were then compared with the respective seasonal 
ADMD daily load curve which shows the similarity of the 
patter.  
REFERENCES 
[1] C. Barbier, A. Maloyd, and G. Putrus, "Embedded controller for 
LV network with distributed generation," Econnect Ventures Ltd., 
Department of Trade and IndustryMay 2007. 
[2] M. MacDonald, "System Integration of Additional Micro-
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http://nrl.northumbria.ac.uk/id/eprint/9225/1/Karmacharya_UPEC_2012.pdf

Evaluation of domestic electrical demand and its effect on low voltage network performance

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